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stat.ML2024
Faster Sampling via Stochastic Gradient Proximal Sampler
Xunpeng Huang, Difan Zou, Yi-An Ma +2
Stochastic gradients have been widely integrated into Langevin-based methods to improve their scalability and efficiency in solving large-scale sampling problems. However, the prox…
stat.ML2024
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
Xunpeng Huang, Difan Zou, Hanze Dong +3
To generate data from trained diffusion models, most inference algorithms, such as DDPM, DDIM, and other variants, rely on discretizing the reverse SDEs or their equivalent ODEs. I…
stat.ML2024
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo
Xunpeng Huang, Difan Zou, Hanze Dong +2
To sample from a general target distribution beyond the isoperimetric condition, Huang et al. (2023) proposed to perform sampling through reverse diffusion, g…